What component of a recurrent neural network (RNN) allows the model to carry previous information to the next iteration?

The component of a recurrent neural network (RNN) that enables the model to carry information from previous iterations to the next is the hidden state. The hidden state is essentially a vector that contains information about the previous inputs and is updated at each time step as the model processes new data. This allows the RNN to maintain a form of memory over sequences, making it particularly effective for tasks that involve time series, natural language processing, or any application where context and historical information are crucial for understanding the current input.

In an RNN, at each time step, the hidden state is influenced by both the current input and the previous hidden state. This interdependence gives RNNs their characteristic ability to remember earlier data points and integrate this information into their predictions. The hidden layer and output layer do not serve this specific function of maintaining a memory of past information; instead, they are involved in processing activations and producing outputs, respectively. The memory block is more commonly associated with architectures like Long Short-Term Memory (LSTM) networks, which are designed to mitigate the vanishing gradient problem in standard RNNs, but the fundamental concept of memory in RNNs is captured by the hidden state.